Tool wear prediction is essential to ensure machining quality and sustainability. Hybrid physics-data Gaussian process regression (GPR) methods integrate domain knowledge with data-driven learning, but a fundamental challenge remains due to an inherent GPR characteristic: when trained on sparse measurements, GPR struggles to extrapolate accurately as tool wear progresses beyond the training distribution, leading to increased uncertainty and prediction errors. This work proposes Gaussian process regression with physics-guided pseudo-samples (GPR-PPS), which addresses this extrapolation issue by enriching the training set with synthetic wear labels at intermediate cuts between sparse measurements. Pseudo-samples are generated by fitting a physics-based flank-wear function to recent GPR predictions and realigning the fitted curve to measured values. These samples are then incorporated into the GPR training set alongside real measurements to predict tool flank wear values across the tool’s operational life. The proposed framework is evaluated on high-speed milling experiments using multi-sensor signals, and the results demonstrate that the proposed method accurately forecasts the entire tool life cycle while using as little as 9.5% of the tool’s life span as initial labeled training data. Compared to conventional machine learning baselines, the proposed approach exhibits superior predictive performance and robustness under limited data conditions.
Nguyen et al. (Wed,) studied this question.